What is UniFrac distance?
The unique fraction metric, or UniFrac, measures the phylogenetic distance between sets of taxa in a phylogenetic tree as the fraction of the branch length of the tree that leads to descendants from either one environment or the other, but not both (Fig. 1).
What is UniFrac PCoA?
UniFrac is a β-diversity measure that uses phylogenetic information to compare environmental samples. UniFrac, coupled with standard multivariate statistical techniques including principal coordinates analysis (PCoA), identifies factors explaining differences among microbial communities.
What does weighted UniFrac measure?
UniFrac is a distance metric used for comparing biological communities. Both weighted (quantitative) and unweighted (qualitative) variants of UniFrac are widely used in microbial ecology, where the former accounts for abundance of observed organisms, while the latter only considers their presence or absence.
What is unweighted UniFrac distance?
There are two versions of UniFrac distances: an unweighted UniFrac distance that considers only species presence and absence information and counts the fraction of branch length unique to either community, and a weighted UniFrac distance that uses species abundance information and weights the branch length with …
What is the difference between weighted and unweighted UniFrac?
Weighted-UniFrac takes into account the relative abundance of species/taxa shared between samples, whereas unweighted-UniFrac only considers presence/absence. Default is FALSE , meaning the unweighted-UniFrac distance is calculated for all pairs of samples.
How is UniFrac calculated?
UniFrac measures the amount of evolutionary divergence between these two communities by dividing the length of the purple branches by the total branch length of the tree.
How do you read PCoA plots?
Interpretation of a PCoA plot is straightforward: objects ordinated closer to one another are more similar than those ordinated further away. (Dis)similarity is defined by the measure used in the construction of the (dis)similarity matrix used as input.
Is Bray Curtis phylogenetic?
First, the Bray-Curtis dissimilarity creates a matrix in function of the how much the samples are different. Whereas the UniFrac measures the phylogenetic distance between sets of taxa in a phylogenetic tree as the fraction of the branch length of the tree that leads to descendants from either one environment.
What do PCoA axes mean?
PCoA starts by putting the first point at the origin, and the second along the first axis the correct distance from the first point, then adds the third so that the distance to the first 2 is correct: this usually means adding a second axis.
How do you describe a PCoA plot?
Principal Coordinates Analysis (PCoA, = Multidimensional scaling, MDS) is a method to explore and to visualize similarities or dissimilarities of data. It starts with a similarity matrix or dissimilarity matrix (= distance matrix) and assigns for each item a location in a low-dimensional space, e.g. as a 3D graphics.
What is the difference between Nmds and PCoA?
NMDS is an iterative method which may return different solution on re-analysis of the same data, while PCoA has a unique analytical solution. The number of ordination axes (dimensions) in NMDS can be fixed by the user, while in PCoA the number of axes is given by the dataset properties (number of samples).
How do you read PCoA?
Is the UniFrac distance metric suitable for multivariate analysis?
UniFrac, coupled with standard multivariate statistical techniques including principal coordinates analysis (PCoA), identifies factors explaining differences among microbial communities. A recent simulation study concluded that UniFrac is unsuitable as a distance metric and should not be used for multivariate analysis ( Schloss, 2008 ).
How is the distance matrix used in PCoA?
This is essentially a measure of how similar or dissimilar the samples are, and is usually represented by a distance matrix which is then used to do Principal Coordinates Analysis (PCoA).
Why do I use weighted UniFrac in QIIME?
There are, of course, several other measures of distance and of scaling/visualisation but these are the ones most commonly used in QIIME. I chose weighted UniFrac (accounting for the abundance of OTUs) over unweighted (ignoring abundance) because I did not observe any patterns by sample type in the unweighted matrix.
What are the formal require ments of UniFrac?
unweighted UniFrac values have the formal require-ments of a distance metric (Rudin, 1987): they arealways non-negative, symmetric and satisfy thetriangle inequality, and identical sequence sets have